TELEMETRY LAYER // VECTOR_CLASSIFICATION
DYNAMIC BIOMETRIC SIGNATURE
& TELEMETRY FORENSICS
Extracting multi-axis stylus tracking paths, auditing pressure transition anomalies, and processing machine learning arrays to resolve fraud.
Visual layout auditing is useless when validating signatures captured across tablet displays, point-of-sale panels, or synchronization pads. Fraud systems can copy static image lines perfectly, but they cannot replicate the signer's personal hidden physiological metrics.
Infinity Forensics runs complete high-fidelity dynamic signature carving. Our hardware metrics engine harvests hidden pen telemetry—including multi-axis tilt coordinates, time pacing velocity, and precise surface friction vectors—processing inputs via Random Forest models to deliver mathematically secure fact configurations.
> STYLUS_METRIC_ACTIVE:
VECTOR_CLASSIFICATION_ONLINE
Our computational analysis engines measure millisecond runtime speed patterns across strokes, calculating reproducible Score-Based Likelihood Ratios (SLR) to verify authentic signatures safely.
R-FOREST
Algorithmic Forgery Identification
SLR-LOG
Objective Probability Score
01 // TELEMETRY PROCESSING PILLARS
Advanced software and statistical auditing models applied to verify digital handwriting metadata.
Stylus Telemetry Extraction
Parsing raw sensory records captured by digitization hardware. We read five-axis variables simultaneously: spatial X/Y pixels, downward surface pressure levels, azimuth rotation angles, and exact tool altitude transitions.
Random Forest Identification
Processing dynamic tracking vectors through machine learning decision trees. The algorithm evaluates variation habits, line entry velocities, and pressure distributions to classify forgeries from authentic user habits.
Score-Based Likelihood Ratios
Converting computational features into mathematical weights. Our lab generates clear probability models that compare the likelihood of signature matching under alternative defense hypotheses for legal clarity.
>> BIOMETRIC VECTOR EXTRACTION LOGS
LOG 01 // EQUITY_AGREE_DISPUTE
Bypassing Copied Image Forgeries
Context: A shareholder signature on a digitized document matching a multi-million dollar equity agreement was disputed. While the line image matched standard profiles perfectly, our telemetry analysis uncurarthed completely flat pressure levels, proving it was copied from a separate file.
LOG 02 // PROCUREMENT_FRAUD_ID
Algorithmic Forgery Identification
Context: A procurement group faced substantial fraud via unauthorized digital payment signs. We processed transaction pen telemetry through our Random Forest models, identifying speed hesitation patterns that exposed manual signature trace manipulation.
LOG 03 // INSURANCE_RECOVERY
Compiling Admissible SLR Probability Reports
Context: An insurer questioned a signature authorization on a tablet system. Our lab compiled reference templates from the owner, computing an objective SLR score that demonstrated a highly definitive mismatch, settling the claim.
Biometric Signature FAQs
Technical answers concerning telemetry extraction mechanics, model criteria parameters, and judicial admissibility values.
1. Are machine learning signature reports admissible in Singapore courts?
Yes. We combine computational findings with established handwriting examination criteria to compile comprehensive, objective expert witness evidence reports compliant with Section 64 of the Evidence Act framework.
2. What distinguishes dynamic biometric signature forensics from standard image audits?
Image audits look purely at static signature outlines. Dynamic forensics reads hidden background telemetry streams recorded by hardware sensors, evaluating velocity maps and pressure paths that cannot be visually forged.
3. What exact parameters are captured by dynamic stylus tracking?
We extract coordinate position grids, pen pressure distributions, pen angle paths relative to the surface, tool rotation details, and exact millisecond timing intervals between individual strokes.
4. Can dynamic signature analysis verify files if the device used to sign was a standard touchscreen?
Basic consumer screens capture position data but omit advanced pressure or angle metadata metrics. While we can audit stroke velocity shapes, precision tracking works best on hardware that records full stylus telemetry.
5. How are Random Forest machine learning models applied to handwriting analysis?
We feed extracted writing vectors into multi-layered decision trees calibrated on handwriting datasets. The model tracks individual micro-variations to classify if inputs display natural muscle behavior patterns.
6. What is a Score-Based Likelihood Ratio (SLR)?
An SLR is a statistical value that evaluates the probability of an event matching under two competing ideas. It answers: how much more likely is this data layout if the owner signed it, versus a proxy attacker copying it?
7. What format should be extracted to initiate a digital signature audit?
We require the native raw telemetry data format container (.json, .xml, or raw binary database strings) holding the background coordinates, rather than flat image exports (.pdf or .png) of the signature outline.
WHY INFINITY FORENSICS ?
Why Infinity Forensics (Private) Limited?
Infinity Forensics has a comprehensive range of computer forensics services to help organizations, and key individuals within those organizations, make informed decisions and mitigate potential electronic evidence risks. We have a highly experienced team of engineers and ways of working that are second to none.
Computer forensics has become increasingly important as fraud, financial irregularities, employee misconduct and commercial disputes threaten company finances and reputations while creating serious regulatory risks.
Utilizing state-of-the-art techniques, Infinity Forensics's specialists enable the recovery and use of critical electronic whether evidence has been erased or modified for litigation, investigations, audits and other fact-finding exercises.